10 citations · 18 across the 30 of their papers we have counts for
6 papers · 1 filter
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Arman Maesumi, Paul Guerrero, Vladimir G. Kim +4
Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can,…
Generative Escher Meshes
Noam Aigerman, Thibault Groueix
This paper proposes a fully-automatic, text-guided generative method for producing perfectly-repeating, periodic, tile-able 2D imagery, such as the one seen on floors, mosaics, cer…
Neural Semantic Surface Maps
Luca Morreale, Noam Aigerman, Vladimir G. Kim +1
We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Lack of annotated data prohibi…
Neural Progressive Meshes
Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman +1
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Inter…
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild
Dafei Qin, Jun Saito, Noam Aigerman +2
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild. Our approach, called Neural Face Rigging (NFR), holds…
TextDeformer: Geometry Manipulation using Text Guidance
William Gao, Noam Aigerman, Thibault Groueix +2
We present a technique for automatically producing a deformation of an input triangle mesh, guided solely by a text prompt. Our framework is capable of deformations that produce bo…